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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Additive Destabilization of Porous Magnesium Borohydride Framework with Core--Shell Structure

Design of interfaces with thermodynamic and kinetic specificity is of great importance for hydrogen storage from both an applied and fundamental perspective. Here, in order to destabilize the metal hydride and protect the dehydrogenated products from oxidizing, a unique core-shell structure of porous Mg(BH4)2-based framework with a thin layer (no more than 5 nm) of MgCl2 additives on the surface, has been proposed and synthesized via a wet-chemical method. The local structure and electronic state of the present complex system are systematically investigated to understand the correlation between the distribution of additives and dehydrogenation property of Mg(BH4)2. A significant improvement is achieved for hydrogen desorption with chlorides: initial hydrogen release from MgCl2 decorated ..gamma..-phase Mg(BH4)2 particles commences at 100 °C and reaches a maximum of 9.4 wt% at 385 °C. Besides the decreased decomposition temperature, an activation barrier of about 76.4 kJ mol-1 lower than that of Mg(BH4)2 without MgCl2 is obtained. Moreover, MgCl2 decoration can also prevent the whole decomposed system (both Mg- and B- elements) from oxidizing, which is a necessary condition to reversibility.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

OC6 Phase IV: Validation of CFD Models for Stiesdal TetraSpar Floating Offshore Wind Platform

ABSTRACT With only a few floating offshore wind turbine (FOWT) farms deployed anywhere in the world, FOWT technology is still in its infancy, building on a modicum of real‐world experience to advance the nascent industry. To support further development, engineers rely heavily on modeling tools to accurately portray the behavior of these complex systems under realistic environmental conditions. This reliance creates a need for verification and validation of such tools to improve reliability of load and dynamic response prediction and analysis capabilities of FOWT systems. The Offshore Code Comparison Collaboration, Continued with Correlation and unCertainty (OC6) project was created under the framework of the International Energy Agency to address this need and considers a three‐sided verification and validation between engineering level models, computational fluid dynamics (CFD), and experimental results. In this paper, a novel floating offshore wind platform, the Stiesdal TetraSpar, is simulated using CFD under the load conditions defined by Phase IV of the OC6 project. The comparison of these CFD results against the experimental results demonstrated the ability to predict the platform response to waves when imposing the measured wave signals as input. Although validation versus experiment was largely successful, the damping behavior was impacted by uncertainties likely originating from the mooring system and sensor umbilical cable. This extensive comparison effort with multiple CFD practitioners offers insight into best practices to achieve reliable results.

17 WIND ENERGY↗
DOE OSTI2025

Investigation of Main Bearing Fatigue Estimate Sensitivity to Synthetic Turbulence Models Using a Novel Drivetrain Model Implemented in OpenFAST

ABSTRACT A coupled medium‐fidelity drivetrain model is developed and implemented in OpenFAST for a 10‐MW land‐based reference turbine. The implementation is verified against a fully coupled multibody wind turbine model, including a detailed drivetrain. The new model can simultaneously and accurately estimate main bearing loads and represent elastic bending of the drivetrain. It has low computational cost and is useful for early design phases, sensitivity analyses and complex systems like wind farms (where computational expense must be expended elsewhere). Here, the model is implemented for a monopile offshore wind turbine and used to investigate the sensitivity of main bearing basic rating life to different synthetic turbulence models. Large‐eddy simulations (LES) targeting stable, neutral, and unstable atmospheric conditions at below‐, near‐ and above‐rated wind speeds are used as a reference. The turbulence models recommended by the International Electrotechnical Commission, the Mann spectral tensor model, and the Kaimal spectral model with exponential coherence are fitted to the LES data. Additionally, a constrained turbulence generator, PyConTurb (short for Python Constrained Turbulence ), based on LES data, is applied in the aero‐hydro‐servo‐elastic simulations. Taking PyConTurb as the baseline, the Kaimal model significantly underestimates fatigue of the downwind main bearing, with between 10% and 40% less damage. The Mann model also underestimates the downwind main bearing fatigue by up to 30%. The upwind main bearing damage is driven by mean loads, and differences between models are less significant, although the trends are similar. Reasons for these discrepancies are investigated and attributed to differences in spatial and temporal variations among the turbulence models.

17 WIND ENERGY↗
DOE OSTI2026

Toward real-time optimization through model reduction and model discrepancy sensitivities

Optimization problems arise in a range of scenarios, from optimal control to model parameter estimation. In many applications, such as the development of digital twins, it is essential to solve these optimization problems within wall-clock-time limitations. However, this is often unattainable for complex systems, such as those modeled by nonlinear partial differential equations. One strategy for mitigating this issue is to construct a reduced-order model (ROM) that enables more rapid optimization. In particular, the use of nonintrusive ROMs—those that do not require access to the full-order model at evaluation time—is popular because they facilitate the computation of optimization solutions within the wall-clock time requirements. However, the optimization solution will be unreliable if the iterates move outside the ROM training data. This article proposes the use of hyper-differential sensitivity analysis with respect to model discrepancy (HDSA-MD) as a computationally efficient tool to augment ROM-constrained optimization and improve its reliability. The proposed approach consists of two phases: (i) an offline phase where several full-order model evaluations are computed to train the ROM, and (ii) an online phase where a ROM-constrained optimization problem is solved, a limited number of full-order model evaluations are computed, and HDSA-MD is used to enhance the optimization solution. Numerical results are demonstrated for two examples, atmospheric contaminant control and wildfire ignition location estimation, in which a ROM is trained offline using inaccurate atmospheric data. In conclusion, the HDSA-MD update yields a significant improvement in the ROM-constrained optimization solution using only one full-order model evaluation online with corrected atmospheric data.

PDE-constrained optimization↗
DOE OSTI2025

New approaches to secondary metabolite discovery from anaerobic gut microbes

The animal gut microbiome is a complex system of diverse, predominantly anaerobic microbiota with secondary metabolite potential. These metabolites likely play roles in shaping microbial community membership and influencing animal host health. As such, novel secondary metabolites from gut microbes hold significant biotechnological and therapeutic interest. Despite their potential, gut microbes are largely untapped for secondary metabolites, with gut fungi and obligate anaerobes being particularly under-explored. To advance understanding of these metabolites, culture-based and (meta)genome-based approaches are essential. Culture-based approaches enable isolation, cultivation, and direct study of gut microbes, and (meta)genome-based approaches utilize in silico tools to mine biosynthetic gene clusters (BGCs) from microbes that have not yet been successfully cultured. In this mini-review, we highlight recent innovations in this area, including anaerobic biofoundries like ExFAB, the NSF BioFoundry for Extreme & Exceptional Fungi, Archaea, and Bacteria. These facilities enable high-throughput workflows to study oxygen-sensitive microbes and biosynthetic machinery. Such recent advances promise to improve our understanding of the gut microbiome and its secondary metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Synthetic microbial communities: Bridging research and application in second-generation bioenergy feedstock microbiomes

The sustainable production of purpose-grown bioenergy feedstocks is essential in transitioning away from fossil fuels. Synthetic communities (SynComs) are consortia of microorganisms that can be used as biological interventions to support objectives like plant growth and stress tolerance. This review examines the state of knowledge regarding microbiomes and SynComs of second-generation bioenergy feedstocks, focusing on the rhizosphere. We first provide an overview of second-generation feedstocks, including switchgrass (Panicum virgatum), miscanthus (Miscanthus × giganteus), sorghum (Sorghum spp.), sugarcane (Saccharum spp.), and poplar (Populus spp.), and summarize our current understanding of their plant-soil-microbiome ecology. We next discuss considerations in the objectives, design, and evaluation of SynComs to enhance feedstock production, and then critically review the literature around their use. Our literature analysis revealed that SynCom performance varied substantially between controlled pilot experiments and field trials, possibly due to system complexity that could not be fully considered in their design and pilot evaluation. We identified a gap in the use of SynComs to support the unique sustainability objectives of biofuel feedstock agriculture, presenting an opportunity to leverage these additional microbial traits in SynCom designs. Finally, we emphasize the importance of targeted research to identify the ecological principles that govern the assembly, activation, and persistence of microbes in the feedstock rhizosphere, thereby enhancing our capacity to manage microbiomes under diverse environmental conditions and ensure their functionality. Beyond biofuels, SynComs are a promising microbiome management strategy for crop production; however, an ecologically informed design and evaluation of SynComs are advised.

SynCom↗
DOE OSTI2023

Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection

Abstract Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar , which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Knowledge-matching based computational framework for genome-scale metabolic model refinement

Genome-scale metabolic models (GEMs) are mathematically structured knowledge base reconstructed from annotated genome of different organisms. With the advancement of next-generation sequencing technology, many organisms have had their genomes sequenced. However, obtaining a high-quality GEM is highly time-consuming, even with the introduction of several genome-scale reconstruction tools that offer automated draft network generation and gap filling. It has been recognized that the iterative process of manual curation and refinement is the limiting step of GEM development, and how to expedite the GEM refinement is still an open question. As cellular metabolism is a complex system with very high degree of freedom and redundancy, the principles and techniques developed in process systems engineering can be adapted to expedite GEM refinement. In this paper we present a knowledge-matching based computation framework for GEM refinement, and demonstrate the effectiveness of the proposed solution using the refinement of a GEM for Clostridium tyrobutyricum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Spectral Deconvolution of Electron-Bifurcating Flavoproteins

Electron-bifurcating flavoproteins catalyze the tightly coupled reduction of high- and low-potential acceptors using a median-potential electron donor, and are invariably complex systems with multiple redox-active centers in two or more subunits. Methods are described that permit, in favorable cases, the deconvolution of spectral changes associated with reduction of specific centers, making it possible to dissect the overall process of electron bifurcation into individual, discrete steps.

electron bifurcation↗
DOE OSTI2022

Mesoscopic-scale complexity in macroscopically-uniform plastic flow of an Al 0.3 CoCrFeNi high-entropy alloy

The concept of a smooth and homogeneous plastic flow of solids is nowadays constantly challenged by various observations of the self-organization of crystal defects on mesoscopic scales pertaining, e.g., to acoustic emission or the evolution of the local strain field. Such investigations would be of particular interest for High-Entropy Alloys (HEAs) characterized by extremely complex microstructures. However, the complexity of their deformation has been only studied in relation to the jerky flow. The present work explores a mesoscale complexity of the macroscopically-smooth plastic flow of an Al 0.3 CoCrFeNi alloy. Here, an inclusive approach involving various experimental methods and complementary nonlinear analyses allowed to reveal transitions between distinct dynamical regimes, which may be assimilated with noises of different colors, in particular the blue noise that is rarely observed in complex systems, and various kinds of reddened behavior. Such findings provide significant new insights into the micro/macro transition in the deformation behavior of HEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Autonomous anomaly detection of proliferation in the AGN-201 nuclear reactor digital twin

The expansion of global nuclear power necessitates advanced methods for analyzing proliferation indicators. This study introduces a novel application of the Isolation Forest Machine Learning (IFML) algorithm within a digital twin (DT) of the AGN-201 nuclear reactor to autonomously detect anomalies. Leveraging real-time operational data from the AGN-201 DT, the IFML algorithm identifies outliers without prior data labeling and operates as a lightweight, complementary approach to traditional physics-based anomaly detection methods for nuclear safeguards. In a simulated Red vs. Blue team exercise, the IFML algorithm successfully detected six significant unseen anomalies related to reactivity changes, achieving an accuracy of 99% for identifying operational deviationxs. These anomalies, caused by deliberate perturbations, were detected alongside known physics-based models, underscoring the potential of IFML to enhance real-time monitoring without displacing traditional methods. Further, this study highlights the applicability of IFML in nuclear environments by providing an additional, redundant layer of anomaly detection to improve safeguards and operational safety in complex systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A hybrid smoothed-particle hydrodynamics model of oxide skins on molten aluminum

A computational model of aluminum melting is proposed which captures both the thermal fluid-solid phase transition and the mechanical effects of oxidation. The model hybridizes ideas from smoothed particle hydrodynamics and bonded particle models to simulate both hydrodynamic flows and solid elasticity. Oxidation is represented by dynamically adding and deleting spring-like bonds between surface fluid particles to represent the formation and rupture of the oxide skin. Various complex systems are simulated to demonstrate the adaptability of the method and to illustrate the significant impact of skin properties on material flow. As a result, initial comparison to experiments of a melting aluminum cantilever highlights that the computational model can reproduce key qualitative features of aluminum relocation.

Clemmer, Joel Thomas↗
DOE OSTI2026

High-throughput validation of phase formability and simulation accuracy of Cantor alloys

High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a function of composition and temperature. However, the integration of computational predictions with experimental validation remains challenging in high-throughput studies. In this work, we introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations, providing a quantitative measure of the confidence of machine learning models trained on either DFT or CALPHAD input in accounting for experimental evidence. The experimental dataset was generated via high-throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries, heated from room temperature to ~1000 °C. Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation. This integrated approach demonstrates where strong overall agreement between computation and experiment exists, while also identifying key discrepancies, particularly in FCC/BCC predictions at Mn-rich regions to inform future model refinement.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

Unleashing the capacity of Rhodococcus for converting lignin into lipids

Bioconversion of bioresources/wastes (e.g., lignin, chemical pulping byproducts) represents a promising approach for developing a bioeconomy to help address growing energy and materials demands. Rhodococcus, a promising microbial strain, utilizes numerous carbon sources to produce lipids, which are precursors for synthesizing biodiesel and aviation fuels. However, compared to chemical conversion, bioconversion involves living cells, which is a more complex system that needs further understanding and upgrading. Various wastes amenable to bioconversion are reviewed herein to highlight the potential of Rhodococci for producing lipid-derived bioproducts. In light of the abundant availability of these substrates, Rhodococcus’ metabolic pathways converting them to lipids are analyzed from a “beginning-to-end” view. Based on an in-depth understanding of microbial metabolic routes, genetic modifications of Rhodococcus by employing emerging tools (e.g., multiplex genome editing, biosensors, and genome-scale metabolic models) are presented for promoting the bioconversion. Co-solvent enhanced lignocellulose fractionation (CELF) strategy facilitates the generation of a lignin-derived aromatic stream suitable for the Rhodococcus’ utilization. Novel alkali sterilization (AS) and elimination of thermal sterilization (ETS) approaches can significantly enhance the bioaccessibility of lignin and its derived aromatics in aqueous fermentation media, which promotes lipid titer significantly. In order to achieve value-added utilization of lignin, biodiesel and aviation fuel synthesis from lignin and lipids are further discussed. In conclusion, the possible directions for unleashing the capacity of Rhodococcus through synergistically modifying microbial strains, substrates, and fermentation processes are proposed toward a sustainable biological lignin valorization.

09 BIOMASS FUELS↗
DOE OSTI2021

Critical analysis of velocimetry methods for particulate flows from synthetic data

Particle tracking methods that extract high-fidelity particle velocity data from high speed video of particle laden flows is a common experimental technique applied to chemical processes. These measurements are used to better understand the motion of particles and fluids in complex systems and create data against which computational models are validated. However, the methods, codes, and experimental setups all have limitations. It is imperative that practitioners verify the methods and their implementation as well as understand the limitations of experimental setups. This work focuses on quantifying the visible depth of field in a high particle concentration fluidized bed. Following a precedent set by the particle imaging velocimetry community, a particle velocity field is manufactured using a computational fluid dynamics and discrete element method simulation. Photo realistic high-speed videos are rendered based on the simulated data using the three-dimensional creation software Blender. Particle velocities are extracted from the synthetic high-speed videos using three variants of Particle Tracking Velocimetry and Optical Flow Velocimetry methodologies. Here, the tracked results are then compared to the known solution, quantifying the error associated with the assumed visible depth. The results indicate that at depth of one particle diameter, all three particle tracking codes give accurate measurements, largely within 5%. However, the error increases when the full bed video measurements are compared to the known solution at one particle diameter, i.e., mimicking a validation study. Finally, for some statistics the constant depth assumption only increases the error slightly, for others significantly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Chemistry of the interaction between an alkoxysilane-based impregnation treatment and cementitious phases

Chemical compatibility with a wide range of materials is among the features that has driven the use of alkoxysilanes as consolidants in built structures. Such compatibility is particularly important in cementitious materials where the reaction with portlandite may generate C-S-H gel, one of the main hydration phases of OPC. The cementitious matrix is a complex system, however, and the reaction of its many phases with alkoxysilanes, while poorly understood, may determine treatment efficacy. This article describes a detailed study of the individual interactions between an oligomeric alkoxysilane-based impregnation treatment previously shown to interact with the portlandite present in cement paste and the cementitious phases generated in ordinary portland cement hydration. The findings show that both portlandite and C-S-H gel interact with the silicon oligomers in the hydrolysed impregnation treatment to generate a C-S-H gel (in the case of portlandite) and a rise in C-S-H gel mean chain length (MCL). Ettringite is also altered in the presence of alkoxysilanes, transforming to gypsum and AH{sub 3}. Its transformation generates a tetrahedral aluminium that is taken up into a high silicon gel sourced from the treatment to form an amorphous aluminosilicate gel. Monocarboaluminate and katoite also partially decompose in the interaction with the product, whereas gibbsite remains unaffected.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evidence of formation of an amorphous magnesium silicate (AMS) phase during alkali activation of (Na-Mg) aluminosilicate glasses

Highlights: • No hydrotalcite-group phase is formed as Al preferentially incorporates into zeolites. • Mg forms an amorphous Mg silicate (AMS) phase. • AMS is a more depolymerized phase when compared to Mg silicate hydrate (M-S-H). • High charge density of Mg effectively stabilizes depolymerized silicates in AMS phase. • Existence of AMS phase should be considered as possibility in Mg-rich AAMs. There is some ambiguity regarding the fate of Mg during the alkali activation of Mg-rich precursors within the broader field of alkali activated materials (AAMs). The present work addresses this issue by studying the reaction products in AAMs synthesized from (Na-Mg) aluminosilicate glasses. Here, instead of magnesium silicate hydrate (M-S-H) phase, Mg exclusively forms an amorphous magnesium silicate (AMS) phase. Compared to M-S-H, AMS is a more depolymerized phase, which has not previously been well documented. The formation of AMS seems to be driven by the high charge density of the Mg cation which effectively stabilizes the depolymerized silicate species. We also show that the lack of hydrotalcite-group phases is due to aluminum depletion by zeolite formation. This work highlights the need to consider the existence of the AMS phase in Mg-containing AAMs, especially in complex systems, where its identification may be difficult.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A simple and practical process modeling methodology for pressure swing adsorption

Although many dynamic models exist for the design and simulation of pressure swing adsorption (PSA) processes, these models involve the solution of a complex system of coupled partial differential equations. Process engineers need a simple, practical, and yet robust short-cut model that helps decide whether to implement a PSA system in a process flowsheet. This work presents a “virtual” moving bed modeling methodology that considers only mass and energy balances and adsorption isotherms to describe the cyclic steady state behavior of PSA systems. Similar to tray efficiencies in distillation calculations, adsorption efficiencies are further introduced to account for system “non-ideality.” Finally, a lab-scale air separation system is used to illustrate the application of this modeling methodology.

42 ENGINEERING↗